Method and system for determining context-based subscription product suite in ride-hailing platforms

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a plurality of context-based subscription products in a ride-hailing platform are described. An exemplary method may include: determining a plurality of transportation contexts to which a plurality of context-based subscription products are applicable; obtaining a plurality of historical trip-requesting sessions between a plurality of riders and the ride-hailing platform, and a plurality of historical subscription products offered to the plurality of riders; constructing at least one key performance indicator (KPI) model that predicts a reward for the ride-hailing platform offering the plurality of context-based subscription products to the plurality of riders; constructing an optimization model to maximize a weighted sum of the at least one KPI model; and determining optimal values of the plurality of parameter vectors corresponding to the plurality of context-based subscription products.

TECHNICAL FIELD

The disclosure relates generally to systems and methods for determiningsubscription products in ride-hailing platforms, in particular,determining a context-based subscription suite in a ride-hailingenvironment.

BACKGROUND

Subscription products have been widely used in the ridesharing industry.A typical subscription product may provide a user with certain forms ofdiscount for the trips at the cost of a premium. The design of pricingand other terms is critical in delivering a successful subscriptionproduct. A ridesharing platform might provide various subscriptionproducts for users to purchase. However, they usually follow some simplerules, such as $8 for a weekly subscription, $25 for a monthlysubscription, 10% off when a rider takes more than a certain amount ofrides. These subscriptions are usually context-blind, i.e., thepromotion/discount applies to all the trips a rider takes, and it won'tadjust itself for trips under different contexts (e.g., spatialcontexts, temporal contexts, weather contexts). Therefore, users may nothave the incentives to purchase multiple subscriptions during the sameperiod. More importantly, these coarse-grained pricing/terms strategiesare inefficient in tailoring the subscription products according to eachuser's specific needs under different contexts. Therefore, it isdesirable to design a novel way to provide a context-based subscriptionsuite for users using ride-hailing platforms.

SUMMARY

Various embodiments of the present specification may include systems,methods, and non-transitory computer-readable media for constructing avirtual environment for a ride-hailing platform.

According to one aspect, a method for determining a context-basedsubscription suite in a ride-hailing platform is provided. The methodmay comprise: constructing a plurality of parameter vectors eachrepresenting a context-based subscription product, wherein each of theplurality of context-based subscription products corresponds to atransportation context, and when a transportation trip requested througha ride-hailing platform satisfies one of the plurality of transportationcontexts, the corresponding context-based subscription sets a benefit tobe applied to the transportation trip; obtaining a plurality ofhistorical trip-requesting sessions between a plurality of riders andthe ride-hailing platform, and a plurality of historical context-basedsubscription products offered to the plurality of riders; training,based on the plurality of historical trip-requesting sessions and theplurality of context-based historical subscription products, at leastone key performance indicator (KPI) model that predicts a KPI value forthe ride-hailing platform, wherein the KPI value indicates a reward tothe ride-hailing platform for offering the plurality of context-basedsubscription products to the plurality of riders of the plurality ofhistorical trip-requesting sessions, and is predicted based at least onthe plurality of parameter vectors; constructing an optimization modelto maximize a weighted sum of the at least one KPI value generated bythe at least one KPI model, wherein the optimization model comprises theplurality of parameter vectors as decision variables; and determining,by the computing device solving the optimization model, an optimalconfiguration for each of the plurality of parameter vectors, whereinthe optimal configuration comprises an optimal premium and an optimalbenefit of the context-based subscription product corresponding to theeach parameter vector.

According to another aspect, a system for determining a context-basedsubscription suite in a ride-hailing platform is provided. The systemmay comprise one or more processors and one or more non-transitorycomputer-readable memories coupled to the one or more processors, theone or more non-transitory computer-readable memories storinginstructions that, when executed by the one or more processors, causethe system to perform operations comprising: constructing a plurality ofparameter vectors each representing a context-based subscriptionproduct, wherein each of the plurality of context-based subscriptionproducts corresponds to a transportation context, and when atransportation trip requested through a ride-hailing platform satisfiesone of the plurality of transportation contexts, the correspondingcontext-based subscription sets a benefit to be applied to thetransportation trip; obtaining a plurality of historical trip-requestingsessions between a plurality of riders and the ride-hailing platform,and a plurality of historical context-based subscription productsoffered to the plurality of riders; training, based on the plurality ofhistorical trip-requesting sessions and the plurality of context-basedhistorical subscription products, at least one key performance indicator(KPI) model that predicts a KPI value for the ride-hailing platform,wherein the KPI value indicates a reward to the ride-hailing platformfor offering the plurality of context-based subscription products to theplurality of riders of the plurality of historical trip-requestingsessions, and is predicted based at least on the plurality of parametervectors; constructing an optimization model to maximize a weighted sumof the at least one KPI value generated by the at least one KPI model,wherein the optimization model comprises the plurality of parametervectors as decision variables; and determining, by the computing devicesolving the optimization model, an optimal configuration for each of theplurality of parameter vectors, wherein the optimal configurationcomprises an optimal premium and an optimal benefit of the context-basedsubscription product corresponding to the each parameter vector.

According to yet another aspect, a non-transitory computer-readablestorage medium for determining a context-based subscription suite in aride-hailing platform is provided. The storage medium may storeinstructions that, when executed by one or more processors, cause theone or more processors to perform operations comprising: constructing aplurality of parameter vectors each representing a context-basedsubscription product, wherein each of the plurality of context-basedsubscription products corresponds to a transportation context, and whena transportation trip requested through a ride-hailing platformsatisfies one of the plurality of transportation contexts, thecorresponding context-based subscription sets a benefit to be applied tothe transportation trip; obtaining a plurality of historicaltrip-requesting sessions between a plurality of riders and theride-hailing platform, and a plurality of historical context-basedsubscription products offered to the plurality of riders; training,based on the plurality of historical trip-requesting sessions and theplurality of context-based historical subscription products, at leastone key performance indicator (KPI) model that predicts a KPI value forthe ride-hailing platform, wherein the KPI value indicates a reward tothe ride-hailing platform for offering the plurality of context-basedsubscription products to the plurality of riders of the plurality ofhistorical trip-requesting sessions, and is predicted based at least onthe plurality of parameter vectors; constructing an optimization modelto maximize a weighted sum of the at least one KPI value generated bythe at least one KPI model, wherein the optimization model comprises theplurality of parameter vectors as decision variables; and determining,by the computing device solving the optimization model, an optimalconfiguration for each of the plurality of parameter vectors, whereinthe optimal configuration comprises an optimal premium and an optimalbenefit of the context-based subscription product corresponding to theeach parameter vector.

These and other features of the systems, methods, and non-transitorycomputer-readable media disclosed herein, as well as the methods ofoperation and functions of the related elements of structure and thecombination of parts and economies of manufacture, will become moreapparent upon consideration of the following description and theappended claims with reference to the accompanying drawings, all ofwhich form a part of this specification, wherein like reference numeralsdesignate corresponding parts in the various figures. It is to beexpressly understood, however, that the drawings are for purposes ofillustration and description only and are not intended as a definitionof the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary system to which determining acontext-based subscription suite in a ride-hailing platform may beapplied, in accordance with various embodiments.

FIG. 2 illustrates a diagram of context-based subscription products in aride-hailing platform in accordance with various embodiments.

FIG. 3 illustrates an exemplary diagram of an optimization model fordetermining a context-based subscription suite in a ride-hailingplatform in accordance with various embodiments.

FIG. 4 illustrates an exemplary diagram for training prediction modelsto determine optimal context-based subscription products in aride-hailing platform in accordance with various embodiments.

FIG. 5 illustrates an exemplary method for determining a context-basedsubscription suite in a ride-hailing platform in accordance with variousembodiments.

FIG. 6 illustrates another exemplary method for determining acontext-based subscription suite in a ride-hailing platform inaccordance with various embodiments.

FIG. 7 illustrates an example computing device in which any of theembodiments described herein may be implemented.

DETAILED DESCRIPTION

Specific, non-limiting embodiments of the present invention will now bedescribed with reference to the drawings. It should be understood thatparticular features and aspects of any embodiment disclosed herein maybe used and/or combined with particular features and aspects of anyother embodiment disclosed herein. It should also be understood thatsuch embodiments are by way of example and are merely illustrative of asmall number of embodiments within the scope of the present invention.Various changes and modifications obvious to one skilled in the art towhich the present invention pertains are deemed to be within the spirit,scope, and contemplation of the present invention as further defined inthe appended claims.

This specification provides various methods, systems, and storagemediums to extend subscription products for a ride-hailing platform tobe context-based (e.g., may also be called context-aware). For example,a trip order requesting a ride may fall into a pre-definedtransportation context (e.g., rainy day, from a residential area tocommercial area), and may receive a discount if the rider has purchaseda subscription product corresponding to the pre-defined context. Thisway, a user may be incentivized to purchase multiple subscriptionproducts (also called “subscription product suite”) at a time.

In some embodiments, the specification also discloses methods andsystems for joint model training and optimization for context-basedsubscription product suite in ride-hailing platforms. The disclosedmethods and systems are configured to determine the optimal parametersof the subscription products, such as premium and other terms of thesubscription products. For example, by leveraging the context-baseddiversity in user behaviors and other important attributes related toridesharing, the disclosed embodiments provide a way to algorithmicallydesign premiums, discounts and other terms for subscription productsthat users are willing to purchase, with an objective to maximize one ormore key performance indicator (KPI) values that the subscriptionproducts may bring to the ridesharing platform.

In some embodiments, each subscription product may be defined by aparameter vector including a set of parameters. For example, theparameter vector may include a premium parameter (e.g., an amount that arider need to pay upfront in order to receive the benefits from thesubscription product), one or more parameters defining benefits of thesubscription product, one or more parameters defining the limitations togain the benefits (e.g., the expiration date of the subscriptionproduct), and one or more parameters defining the context constraintsfor the subscription product (e.g., conditions for the subscriptionproduct to be applicable). The benefits of the subscription product mayinclude a discount (e.g., an amount or a percentage of reduction off thedisplayed price of an order, or an upfront discount for lump sumdeposit) or another type of incentive reward. The limitations to receivethe benefits (discounts) may include a period during which the purchasermay receive the benefits (e.g., a monthly pass, a weekly pass), a tripcount limit (e.g., the benefits will apply to the trips up to a tripcount limit), or other suitable limitations.

In some embodiments, the context constraints for the subscriptionproduct define a plurality of transportation contexts. They may includea spatial condition, a route condition, a temporal condition, aspatial-temporal condition, a route-temporal condition, a weathercondition, a marketplace condition, another suitable condition, or anycombination thereof. The spatial condition may define the origin regionor destination region for a trip to receive the benefits of thesubscription product, e.g., the subscription product may be applied to atrip from or to a certain commercial area. The route condition maydefine a specified route (a pair of origin region and destinationregion), and the trips with the specified route may receive the benefitsof the subscription product, e.g., from home to work. The temporalcondition may define a specified time range, and trips that occurredduring this range may receive the benefits of the subscription product.The spatial-temporal condition may refer to any pair of a spatialcondition and a temporal condition as described above. Theroute-temporal condition may refer to any pair of a route condition anda temporal condition as described above. The weather condition may referto a following condition, e.g., rainy, stormy, temperature over 90degrees Fahrenheit, and trips satisfying the weather condition mayreceive the benefits of the subscription product. The marketplacecondition may refer to the current supply-demand condition of themarket, e.g., when the surge multiplier is higher than a pre-definedthreshold (which indicates “under-supply” imbalance), and tripsoccurring under the defined marketplace condition may receive thebenefits of the subscription product.

FIG. 1 illustrates an exemplary system 100 to which determining acontext-based subscription suite in a ride-hailing platform may beapplied, in accordance with various embodiments. The exemplary system100 may include a computing system 102, a computing device 104, and acomputing device 106. It is to be understood that although two computingdevices are shown in FIG. 1, any number of computing devices may beincluded in the system 100. Computing system 102 may be implemented inone or more networks (e.g., enterprise networks), one or more endpoints,one or more servers, or one or more clouds. A server may includehardware or software which manages access to a centralized resource orservice in a network. A cloud may include a cluster of servers and otherdevices which are distributed across a network.

The computing devices 104 and 106 may be implemented on or as variousdevices such as a mobile phone, tablet, server, desktop computer, laptopcomputer, vehicle (e.g., car, truck, boat, train, autonomous vehicle,electric scooter, electric bike), etc. The computing system 102 maycommunicate with the computing devices 104 and 106, and other computingdevices. Computing devices 104 and 106 may communicate with each otherthrough computing system 102, and may communicate with each otherdirectly. Communication between devices may occur over the internet,through a local network (e.g., LAN), or through direct communication(e.g., BLUETOOTH™, radio frequency, infrared).

In some embodiments, the system 100 may include a ride-hailing platform.The ride-hailing platform may facilitate transportation service byconnecting drivers of vehicles with passengers. The platform may acceptrequests for transportation from passengers, identify idle vehicles tofulfill the requests, arrange for pick-ups, and process transactions.For example, passenger 140 may use the computing device 104 to order atrip. The trip order may be included in communications 122. Thecomputing device 104 may be installed with a software application, a webapplication, an API, or another suitable interface associated with theride-hailing platform.

While the computing system 102 is shown in FIG. 1 as a single entity,this is merely for ease of reference and is not meant to be limiting.One or more components or one or more functionalities of the computingsystem 102 described herein may be implemented in a single computingdevice or multiple computing devices. In some embodiments, the computingdevice 104 may include a context-based subscription productsconstruction component 112, a training data collection component 114, anoptimization component 116, a prediction models training component 118,and a context-based subscription determination component 119.

In some embodiments, the context-based subscription productsconstruction component 112 may be configured to construct a plurality ofparameter vectors each representing a context-based subscriptionproduct, wherein each of the plurality of context-based subscriptionproducts corresponds to a transportation context, and when atransportation trip requested through a ride-hailing platform satisfiesone of the plurality of transportation contexts, the correspondingcontext-based subscription sets a benefit to be applied to thetransportation trip. Here, the “benefit” may include a discount, areward, an incentive, or another form of benefit. Each of the pluralityof context-based subscription products may be applicable to atransportation trip-requesting session between a rider and theride-hailing platform when the trip-requesting session satisfies theconditions of the transportation context corresponding to thecontext-based subscription product. As described above, each of thetransportation contexts may be defined by a set of context constraints(conditions) including a spatial condition, a route condition, atemporal condition, a spatial-temporal condition, a route-temporalcondition, a weather condition, a marketplace condition, anothersuitable condition, or any combination thereof. For simplicity, theembodiments disclosed herein may assume the transportation contexts arealready determined or may be directly obtained. It may be obvious for aperson in the art to use other traditional clustering methods todetermine the routes and time conditions, or to determine the “highsurge” threshold by analyzing historical price surge data.

In some embodiments, the training data collection component 114 may beconfigured to obtain a plurality of historical trip-requesting sessionsbetween a plurality of riders and the ride-hailing platform, and aplurality of historical context-based subscription products offered tothe plurality of riders. Here, each of the historical trip-requestingsessions may include a rider's browsing event for a ride (e.g.,including origin, destination, time, and other suitable information),the displayed information from the ride-hailing platform (e.g., rideoptions, route selection, prices for the ride options, and othersuitable information displayed on the rider's smartphone), the rider'srequest for the ride (e.g., whether the rider decides to convert thebrowsing event to a trip), other suitable information, or anycombination thereof. These historical trip-requesting sessions may beused by other components to train various machine learning models andevaluate the trained machine learning models.

In some embodiments, the optimization component 116 may be configured tolearn at least one key performance indicator (KPI) model that predicts aKPI value for the ride-hailing platform, wherein the KPI value indicatesa reward to the ride-hailing platform for offering the plurality ofcontext-based subscription products to the plurality of riders of theplurality of historical trip-requesting sessions, and is predicted basedat least on the plurality of parameter vectors; and construct anoptimization model to maximize a weighted sum of the at least one KPIvalue generated by the at least one KPI model, wherein the optimizationmodel comprises the plurality of parameter vectors of the plurality ofcontext-based subscription products as decision variables.

In some embodiments, the KPI models may include a net income model, agross merchandise value (GMV) model, a number of trips model, anothersuitable model, or any combination thereof. In some embodiments, theoptimization model may be represented as formula (1) as below:

max Σ_(i) w_(i)f_(i)(θ_(p), θ_(m), θ_(t), θ_(c), D   (1)

where f_(i) is the i-th KPI models to optimize, w_(i) is the weight fori-th KPI models, θ_(p)=[θ_(p,0), θ_(p,1), . . . , θ_(p,S)]^(T) is thepremium vector for S subscription products, θ_(m) is the discountmultiplier vector for the S subscription products, θ_(t)is the vector ofthe time durations of the S subscription products (weekly, monthly,etc.), θ_(c) is the trip count limit vector for the S subscriptionproducts, and D is the session-level historical data (e.g., theplurality of historical trip-requesting sessions collected by thetraining data collection component 114) that represents the normalmarketplace status. D may be used as the initial status for simulatingthe impact of the subscription products. For the convenience ofnotations, in some embodiments, a dummy subscription product may beincluded in the S subscription products to represent the case where nosubscription is purchased. The parameters for the dummy subscriptionproduct may include: θ_(p,0)=0, θ_(m,0)=1.0; θ_(t,0)=+∞, θ_(c,0)=+∞, orsome sufficiently large number.

In some embodiments, each model f_(i) may be summable or non-summable.The value of a summable model f_(i) may be directly calculated bysumming up the f_(i) value for each of the plurality of historicaltrip-requesting sessions in D. For example, the value of f_(i) may becalculated by: accumulating a plurality of session-level rewardsrespectively corresponding to the plurality of context-basedsubscription products, wherein each of the plurality of session-levelrewards is determined by: determining, by a first trained machinelearning model, a first probability of a rider involved in a historicaltrip-requesting session subscribing one of the plurality ofcontext-based subscription products; determining, by a second trainedmachine learning model, a second probability of the rider converting thehistorical trip-requesting session to a trip; determining an expectedreward assuming the historical trip-requesting session is converted tothe trip; and determining the session-level reward as a product of thefirst probability, the second probability, and the expected reward.

As an example, assuming that for a session, there is at most onesubscription product applicable (if purchased), the i-th model f_(i) maybe calculated as formula (2):

f _(i)(Θ, D)=Σ_(s) Σ_(j)[g _(i)({circumflex over (θ)}_(η(s,j)), E_(j))·p _(conv)(trip|{circumflex over (θ)}_(η(s,i)) , R _(u(j)) , B_(j))·p _(purchase)(s|{circumflex over (θ)} _(s) , E _(u(j)))]  (2)

where subscript s represents the s-th subscription product, subscript jrepresents the row index of the data D, which corresponds to ahistorical trip-requesting session (the j-th trip-requesting session),u(j) refers to the user/rider corresponding to the trip-requestingsession j (in historical data, a user might cover multiple sessions),Θ=[θ_(p), θ_(m), θ_(t), θ_(c)] refers to a plurality of parametervectors defining the plurality of subscription products (every elementhere is a column vector, thus Θ is a matrix; each row of Θ correspondsto one parameter vector defining one of the subscription products),{circumflex over (θ)}_(s)=[θ_(p,s), θ_(m,s), θ_(t,s), θ_(c,s)] definesthe parameter vector for subscription product s, η(s,j)=s·I_({s∈j}),where I_({·}) represents an indicator function, and s ∈ j meanssubscription product s is available for the trip-requesting session j,g_(i)(·) is the same KPI model as f_(i) but evaluated at per-sessionlevel (per trip-requesting session) with the assumption that thecorresponding trip-requesting session has already converted to a trip,E_(j) is a vector of variables that are related to unit economics (e.g.,payment to the driver, charge to the riders, subsidy for drivers andriders, estimated distance of the trip, estimated time duration of thetrip, number of passengers in the trip for carpool services), B_(j) is avector of session-level features that may impact a rider's decision oftaking a ride or not, R_(u) is a vector of rider features for the rideru that may impact a rider's decisions of purchasing a subscriptionproduct, P_(conv) refers to the first trained machine learning model,and P_(conv) (trip|{circumflex over (θ)}_(η(s,j)), R_(u(j)), B_(j)) isthe probability of a trip-requesting session being converted to a trip,P_(purchase) refers to the second trained machine learning model andP_(purchase)(s|{circumflex over (θ)}_(s), R_(u)) is the probability of arider u purchasing the subscription product s. In some embodiments, ahistorical trip-requesting session may trigger one subscription productor none. For simplicity, the transportation contexts are defined asnon-overlapping, and thus one historical trip-requesting session willnot trigger more than one subscription product.

In some embodiments, the value of a non-summable model f_(i) may not bedirectly calculated. For example, a model “profit rate” model defined asprofit/GMV may be non-summable (as summing rates in this context do notmake mathematical sense). Instead of directly calculating the value of anon-summable model f_(i), the summable components may be calculatedfirst using the methods as mentioned above (e.g., formula (2)), and thencalculate the non-summable model by aggregating the values of thesummable components. Still using the “profit rate” defined as profit/GMVas an example, the two summable models (profit and GMV) may becalculated first, then the “profit rate” may be determined by dividingthe values of these two summable models.

In some embodiments, the optimization model may be solved to determinean optimal configuration for each of the plurality of parameter vectors,where the optimal configuration comprises an optimal premium and anoptimal benefit of the context-based subscription product correspondingto the each parameter vector. For example, the optimization modelillustrated in formula (1) may be solved by derivative-free algorithms,including grid search (e.g., an exhaustive searching through a manuallyspecified subset of the hyperparameter space of the optimization model),greedy search (e.g., a heuristic method of searching to the locallyoptimal points in the hyperparameter space of the optimization model),Bayesian optimization (e.g., a sequential design strategy for globaloptimization of black-box functions that do not assume any functionalforms), or another suitable algorithm.

In some embodiments, the prediction models training component 118 may beconfigured to train the conversion model and the purchase model informula (2), where the conversion model predicts if a rider will book atrip given the information the rider observed from the app after therider entered the origin and destination, and the purchase modelpredicts the probability of a rider purchasing each of the subscriptionproducts. In some embodiments, the conversion model and the purchasemodel may be jointly trained by: obtaining a plurality of historicaltrip-requesting sessions between a plurality of riders and aride-hailing platform, wherein each of the historical trip-requestingsessions comprises a plurality of session-level features; obtaining aplurality of parameter vectors respectively corresponding to a pluralityof context-based subscription products; feeding the plurality ofhistorical trip-requesting sessions and the plurality of parametervectors into a first machine learning model to generate a plurality offeature vectors corresponding to the plurality of historicaltrip-requesting sessions, wherein the plurality of feature vectorscomprise a plurality of rider-level features of a rider in each of theplurality of historical trip-requesting sessions and a parameter vectorof one of the plurality of context-based subscription products that isapplicable to the historical trip-requesting session; feeding theplurality of feature vectors and the plurality of session-level featuresof the historical trip-requesting session to a second machine learningmodel to predict a plurality of first probabilities for the plurality ofhistorical trip-requesting sessions being converted to trips;deduplicating the plurality of feature vectors by removing featurevectors with duplicate rider-and-subscription-product pairs to obtain aplurality of deduplicated records of features corresponding to theplurality of riders; feeding the plurality of deduplicated records offeatures to a third machine learning model to predict a plurality ofsecond probabilities of the plurality of riders subscribing one or moreof the plurality of subscription products; jointly training the firstmachine learning model, the second machine learning model, and the thirdmachine learning model.

Based on the denotations in formula (2), the features considered by theconversion model may include the session-level features B_(j), therider-level features R_(u), and the subscription product parametersΘ=[θ_(p), θ_(m), θ_(t), θ_(c)]. In some embodiments, the session-levelfeatures B_(j) may include at least one of the following: a pricedisplayed by the ride-hailing platform to the rider; a discountdisplayed by the ride-hailing platform to the rider; estimated time toarrive (ETA) for pick-up; estimated route distance to a destination;time; or origin and destination locations. In some embodiments, therider-level features R_(u) may include at least one of the following: anaverage price of historical trips; or the rider's subscription history.In some embodiments, the conversion model may be a classification modelfor a plurality of classes corresponding to multiple types ofridesharing services such as a carpool service, a solo service, oranother suitable service. The conversion model may generate theprobability of the predicted class, so that the probability may be usedin the optimization formulation to weigh the different outcomescenarios. In some embodiments, the conversion model may be trained aslogistic regression, a random forest, and neural networks.

Similarly, the purchase model may also be a classification model formultiple classes. The purchase model may generate the probability of thepredicted class. However, the sum of the probabilities may not berequired to be 1, as it is allowed for a user to purchase multiplesubscription products. For example, assuming a neural network is trainedas the purchase model, the output layer of the neural network may beable to generate a “multi-hot” encoded output, rather than a softmaxform (i.e., one-hot encoded). It is because an arbitrary number of ismay be allowed to appear in the final output (e.g., may be upper-boundedby the number of subscription products). In some embodiments, thepurchase model may be trained as logistic regression, a random forest,and neural networks.

In some embodiments, the context-based subscription determinationcomponent 119 may be configured to determine a set of subscriptionproducts to offer to the riders. For example, the optimization modelconstructed by the optimization component 116 (comprising the predictionmodels trained by the prediction models training component 118) may besolved to obtain one or more optimal parameter vectors representing oneor more optimal subscription products. These optimal subscriptionproducts may theoretically generate a maximized KPI value for theride-hailing platform. The “maximized” KPI value in this context refersto the optimal KPI value calculated based on one or more KPI metrics(may be also referred to as KPI models). In some embodiments, if asmaller value of a KPI metric is preferred (e.g., the ride-hailingplatform may prefer a shorter value for the KPI metric “average waitingtime for pickup”), the KPI metric may be transformed to its negativevalue or inverse value so that the objective is still to “maximize” theKPI value.

In some embodiments, the optimal subscription products may be ranked foreach rider. For example, the ranking may be based on the rider'shistorical travel frequency or the rider's historical spending duringthe specified time blocks. As another example, the trained purchasemodel may provide the probability of how likely a rider will purchaseeach of the subscription products, which may be used to rank thesubscription products for the rider.

FIG. 2 illustrates a diagram of context-based subscription products in aride-hailing platform in accordance with various embodiments. As shown,a ride-hailing platform 220 may provide a plurality of subscriptionproducts 250 for the riders 210. These subscription products 250 may becontext-based or context-aware, meaning one subscription product 250 maybe specifically designed for (e.g., applicable to) one of thetransportation contexts 240. The transportation contexts 240 may bepredetermined by the ride-hailing platform 220 in various ways. Forexample, each transportation context may include one or more conditions,and when a trip-requesting session between a rider and the ride-hailingplatform satisfies the one or more conditions of one of the plurality oftransportation contexts, the trip-requesting session may belong to thetransportation context and receive a discount according to one of theplurality of context-based subscription products that is applicable tothe one transportation context. In some embodiments, the one or moreconditions of each transportation context comprise at least one of thefollowing: a spatial condition defining an origin region or adestination region; a route condition defining a pair of origin regionand destination region; a temporal condition defining a time range; aspatial-temporal condition comprising any pair of the spatial conditionand the temporal condition; a route-temporal condition comprising anypair of the route condition and the temporal condition; a weathercondition; and a marketplace condition. Each transportation context 240may be mapped to one subscription product 250, and one subscriptionproduct 250 may be mapped to one or more transportation context 240.

When a rider 210 makes a ride request to the ride-hailing platform 220through smartphones or web portals, the rider 210 starts atrip-requesting session 230 with the ride-hailing platform 220. Thetrip-requesting session 230 may be associated with a plurality ofsession-level features including the rider's 210 order details, theride-hailing platform's 220 proposed trip information (e.g., theinformation displayed to the rider through the rider's smartphone), therider's 210 response to the ride-hailing platform's 220 proposed tripinformation, other suitable information, or any combination thereof. Forexample, the rider's 210 order tails may include a pick-up location, adestination, a current time, a trip type (carpool, solo, etc.), apayment method, an applicable coupon, other suitable information, or anycombination thereof. The ride-hailing platform's 220 proposed tripinformation may include ride options, displayed prices, displayeddiscount on the prices, surge multipliers, estimated time to arrival(ETA) for pick-up, estimated distance to arrive for pick-up, estimatedtime to destination (ETD), estimated route distance to the destination,offered subscription products, other suitable information, or anycombination thereof. The rider's 210 response to the ride-hailingplatform's 220 proposed trip information may include whether the requestis converted to an actual trip, whether the rider 210 abandoned therequest, whether the rider 210 purchased one or more of the offeredsubscription products, other suitable information, or any combinationthereof.

Based on the session-level features, a given trip-requesting session 230may be classified into one of the predetermined transportation contexts240. Each of the transportation contexts 240 may be defined by aplurality of conditions, such as a spatial condition, a route condition,a temporal condition, a spatial-temporal condition, a route-temporalcondition, a weather condition, a marketplace condition, anothersuitable condition, or any combination thereof. The spatial conditionmay define the origin region or the destination region for atrip-requesting session to receive the benefits of the subscriptionproduct, e.g., the subscription product may be applied to atrip-requesting session requesting a trip from or to a certaincommercial area. The route condition may define a specified route (apair of origin region and destination region) and the trip-requestingsession requesting the specified route may receive the benefits of thesubscription product, e.g., from home to work. The temporal conditionmay define a specified time range, and a trip-requesting sessionoccurred during this range may receive the benefits of the subscriptionproduct. The spatial-temporal condition may refer to any pair of aspatial condition and a temporal condition as described above. Theroute-temporal condition may refer to any pair of a route condition anda temporal condition as described above. The weather condition may referto a following condition, e.g., rainy, stormy, temperature over 90degrees Fahrenheit, and a trip-requesting session satisfying the weathercondition may receive the benefits of the subscription product. Themarketplace condition may refer to the current supply-demand conditionof the market, e.g., the surge multiplier is above a pre-definedthreshold (which means “under-supply”), and a trip-requesting sessionoccurred under the defined marketplace condition may receive thebenefits of the subscription product.

A trip-requesting session 230 with session-level features satisfying theconditions of a transportation context 240 may be mapped to asubscription product 250 corresponding to the transportation context240. For example, in FIG. 2, if a trip-requesting session 230 satisfiestransportation context #1, subscription #1 may be applicable to thetrip-requesting session. In practice, a rider may make multiple riderequests during a short period of time that are categorized intodifferent transportation contexts, and thus different subscriptionproducts may be applicable. For example, a rider may book a first tripfrom home to work in the morning that may satisfy a transportationcontext defined by a route-temporal condition. The rider may then book asecond trip to the airport during the rush hour that may satisfy atransportation context defined by a marketplace condition. Subsequently,the rider may book a third trip to a hotel during rainy weather that maysatisfy a transportation context defined by a weather condition. In thiscase, the rider may have incentives to purchase various subscriptionproducts to cover his or her trips under different transportationcontexts and maximize the savings.

FIG. 3 illustrates an exemplary diagram of an optimization model 310 fordetermining a context-based subscription suite in a ride-hailingplatform in accordance with various embodiments. In some embodiments,the optimization model 310 may be constructed by: constructing, based onthe plurality of historical trip-requesting sessions and the pluralityof historical subscription products, at least one key performanceindicator (KPI) model that predicts a reward for the ride-hailingplatform offering the plurality of context-based subscription productsto the plurality of riders, wherein the reward is predicted based atleast on a plurality of parameter vectors representing the plurality ofcontext-based subscription products, a plurality of rider-level featuresof each of the plurality of riders, and a plurality of session-levelfeatures of each of the plurality of historical trip-requestingsessions; constructing an optimization model to maximize a weighted sumof the at least one KPI model, wherein the optimization model comprisesthe plurality of parameter vectors of the plurality of context-basedsubscription products as decision variables.

For example, the optimization model 310 comprises an objective function312, one or more KPI models 313-315, one or more prediction models 320that are used in the KPI models 313-315, and a plurality of decisionvariables 330. The objective function 312 of the optimization model 310may be a weighted sum of the KPI models 313-315. An exemplary objectivefunction is illustrated in formula (1). In some embodiments, the KPImodels may include a net income model, a GMV model, a number of tripsmodel, another suitable model, or any combination thereof. Theride-hailing platform may determine the models to be considered and theweights of the models based on its business interest.

In some embodiments, the KPI models 313-315 may be substantiated by oneor more prediction models 320 to predict probabilities that certainactions would occur. These actions may directly result in rewards forthe ride-hailing platform. In some embodiments, the prediction models320 may include a conversion model and a purchase model. As an example,the conversion model and the purchase model may correspond to thep_(conv) and p_(formula) in formula (2), respectively. In someembodiments, the conversion model predicts if a rider will book a tripgiven the information the rider observed from the app after the riderentered the origin and destination. The purchase model predicts theprobability of a rider purchasing each of the subscription products. Insome embodiments, the conversion model and the purchase model may beindividually or jointly trained. An exemplary training process of theconversion model and the purchase model is illustrated in FIG. 4.

In some embodiments, the decision variables 330 of the optimizationmodel 310 may be used to determine the optimal configurations for thecontext-based subscription products. Each subscription product may bedefined by a set of parameters (also called a parameter vector), such asa premium to purchase the product, a discount (as an amount or apercentage) to be applied to trips, a valid period of the product (e.g.,a week, a month), and a trip count limit (e.g., the product can only beapplied to a certain amount of trips). In other embodiments, theconfigurations may have other parameters such as a limit of the totaldiscounts, which may be designed according to the implementationdetails. In some embodiments, the decision variables 330 may be part ofthe objective function 312 (as shown in formula (1)). When the objectivefunction 312 is solved, the values of the decision variables 330 may bedetermined. Referring to the formula (1), the {θ_(p), θ_(m), θ_(t),θ_(c)} may refer to a matrix comprising a plurality of rows, each rowincluding a parameter vector that defines a subscription product.Assuming the ride-hailing platform determines that there are Stransportation contexts, the matrix {θ_(p), θ_(m)m θ_(t), θ_(c) } mayinclude S rows. By solving the optimization function 312, a maximum KPIvalue may be obtained by the determined values of the {θ_(p), θ_(m),θ_(t), θ_(c)}. These determined values may define the S subscriptionproducts that the ride-hailing platform can offer to the riders.

In some embodiments, the optimization model 310 may be evaluated basedon a plurality of historical communication sessions 360. The historicalcommunication sessions 360 may be treated as a reflection of currentmarket status, which provides a simulated environment to evaluate thesubscription products, e.g., the parameter D in formula (1) may providea simulated market place to determine how the {θ_(p), θ_(m), θ_(t),θ_(c)} in formula (1) may affect the weighted sum of the For example,the historical communication sessions 360 may include a plurality ofhistorical trip-requesting sessions between a plurality of riders andthe ride-hailing platform, a plurality of historical subscriptionproducts offered to the plurality of riders, other suitable information,or any combination thereof.

In some embodiments, among the parameter vectors {θ_(p), θ_(m), θ_(t),θ_(c)} defining the subscription products, the discrete variable θ_(c)may be relaxed to real values. In some embodiments, the KPI models,e.g., Lin formula (1) and g_(i) in formula (2), may not bedifferentiable as they may include various cut-offs and thresholds.Thus, numerical approaches may be used to approximate the gradients.That is, gradient-based or derivative-free optimization algorithms maybe used to solve the optimization model 310, including grid search,greedy search, Bayesian optimization, or another suitable algorithm. Bysolving the optimization model, the optimal values of the plurality ofparameter vectors {θ_(p), θ_(m), θ_(t), θ_(c)} corresponding to theplurality of context-based subscription products may be determined.Based on the optimal values of {θ_(p), θ_(m), θ_(t), θ_(c)}, the optimalcontext-based subscription products may be determined.

In some embodiments, the optimal context-based subscription products maybe sent to riders for purchasing. In order to avoid unnecessarydisruption or confusion, the ride-hailing platform may selectively sendsome of the optimal context-based subscription products to a given riderbased on the rider's features and conditions. For example, a givenrider's historical trips may be analyzed to determine one or moretransportation contexts that the rider may be frequently use. Spatialand temporal features of the rider's historical trips may be extractedand compared against the conditions of each of the transportationcontext, such as, the rider took trips between home and work frequently,the rider took trips during rainy days frequently, and the rider visiteda specific shopping mall frequently. Then the ride-hailing platform mayrecommend the optimal context-based subscription products correspondingto the one or more transportation contexts to the rider for purchasing.In some embodiments, a rider's foreseeable status change may beconsidered in recommending optimal context-based subscription products.For example, if the rider's trip to another location is observed, theride-hailing platform may recommend optimal context-based subscriptionproducts that are suitable for the riders in that location (e.g., if thelocation is Seattle during a rainy season, the optimal context-basedsubscription products may include one that provides discount to orderingrides in rainy days).

FIG. 4 illustrates an exemplary diagram for training prediction models320 to determine optimal context-based subscription products in aride-hailing platform in accordance with various embodiments. Thesetrained prediction models 320 may be used as parts of the optimizationmodel 310 shown in FIG. 3 to make predictions of various probabilitiesaccording to input parameters such as subscription product parametervectors, rider-level features, session-level features, other suitableinputs, or any combination thereof.

As described above, these prediction models may include a conversionmodel 440 and a purchase model 450. In some embodiments, the conversionmodel predicts if a rider will book a trip given the information therider observed from the app after the rider entered the origin anddestination. The purchase model predicts the probability of a riderpurchasing (e.g., subscribing) each of the subscription products. Insome embodiments, the prediction models 320 may further include afeature extractor 430 that transforms (e.g., by extracting, selecting,padding, or otherwise converting) the input data into a desired set offeatures to feed into the conversion model 440 and the purchase model450. In some embodiments, the feature extractor 430, the conversionmodel 440, and the purchase model 450 may be individually or jointlytrained.

In some embodiments, the training process for the feature extractor 430,the conversion model 440, and the purchase model 450 may be implementedby: feeding the plurality of sets of rider-level features in thetraining data and a plurality of parameter vectors respectivelycorresponding to a plurality of context-based subscription products to afirst machine learning model (the feature extractor 430) to generate aplurality of feature vectors corresponding to the plurality ofhistorical trip-requesting sessions; feeding the plurality of featurevectors and the plurality of sets of session-level features to a secondmachine learning model (the conversion model 440) to predict a pluralityof first probabilities for the plurality of historical trip-requestingsessions being converted to trips; deduplicating the plurality offeature vectors by removing vectors with duplicaterider-and-subscription-product pairs to obtain a plurality ofdeduplicated records of features corresponding to the plurality ofriders; feeding the plurality of deduplicated records of features to athird machine learning model (the purchase model 450) to predict aplurality of second probabilities of the plurality of riders subscribingone or more of the plurality of subscription products; jointly trainingthe first machine learning model, the second machine learning model, andthe third machine learning model.

In some embodiments, the training data 410 for training the predictionmodels may include a plurality of historical trip-requesting sessionsbetween a plurality of riders and a ride-hailing platform, and aplurality of historical context-based subscription products offered tothe plurality of riders, wherein the plurality of historicalcontext-based subscription products respectively correspond to aplurality of transportation contexts, and when a trip requested by atrip-requesting session satisfies one of the plurality of transportationcontexts, the corresponding context-based subscription applies adiscount to the trip. This training data 410 may refer to (part of) thehistorical communication sessions 360 in FIG. 3. In some embodiments,the training data 410 may be represented as a sample matrix, with eachrow corresponding to a trip-requesting session (each sample) between arider and the ride-hailing platform. In some embodiments (also shown inFIG. 4), the training data 410 may be fed into the feature extractor430, the conversion model 440, and the purchase model 450 along withparameter vectors 420 that define a plurality of subscription products(also called a subscription suite). The prediction models 320 mayinclude a plurality of machine learning models to extract features fromthe input data, and predict probabilities of a trip-requesting sessionconverting to a trip and probabilities of a rider purchasing asubscription product, etc. In some embodiments, the predictionsgenerated by the feature extractor 430, the conversion model 440, andthe purchase model 450 may be compared with the training data (e.g.,some of the training data 410 may be labeled manually or automatically)to obtain rewards, which may be used to adjust the parameters of theprediction models 320 so that the predictions may more closelyapproximate the environment represented by the training data 410.

In some embodiments, the training process may include: feeding theplurality of historical trip-requesting sessions into a first machinelearning model to generate a plurality of feature vectors respectivelycorresponding to the plurality of historical trip-requesting sessions;feeding the plurality of feature vectors to a second machine learningmodel to predict a plurality of first probabilities for the plurality ofhistorical trip-requesting sessions being converted to trips;deduplicating the plurality of feature vectors by keeping one ofmultiple feature vectors corresponding to a same rider and a samesubscription product to obtain a plurality of deduplicated featurevectors respectively corresponding to the plurality of riders; feedingthe plurality of deduplicated feature vectors to a third machinelearning model to predict a plurality of second probabilities of theplurality of riders subscribing one or more of the plurality ofsubscription products; jointly training the first machine learningmodel, the second machine learning model, and the third machine learningmodel based on the plurality of first probabilities, the plurality ofsecond probabilities, and the plurality of historical trip-requestingsessions.

For example, the training data 410 and the parameter vectors 420 may befed into a first machine learning model to generate a plurality offeature vectors corresponding to the plurality of historicaltrip-requesting sessions in the training data 410, wherein the pluralityof feature vectors comprise a plurality of rider-level features of arider in each of the plurality of historical trip-requesting sessionsand a parameter vector of one of the plurality of context-basedsubscription products that is applicable to the historicaltrip-requesting session.

The first machine learning model may refer to a feature extractor 430NN_(feat) (e.g., a neural network) that extracts features. Assuming thesample matrix is denoted as X, thus a j-th row in X corresponding to thej-th trip-requesting session. The NN_(feat) may generate features[θ_(p,ŝ(j)), θ_(m,ŝ(j)), θ_(t,ŝ(j)), θ_(c,ŝ(j)), R_(u(j))] from the j-thtrip-requesting session, where ŝ(j) is defined as the subscriptionproduct s that is applicable at session j (if none is available, ŝ(j),thus [θ_(pŝ(j)), θ_(m,ŝ(j))] refers to the parameter vector of theapplicable subscription product, and R_(u(j)) refers to the rider-levelfeatures of the rider involved in the j-th trip-requesting session.

In some embodiments, the rider-level features of a rider may includerider's historical subscription purchase record, summary statistics ofthe rider's booking behaviors without a subscription, other suitablefeatures, or any combination thereof. The rider's historicalsubscription purchase record may include the subscription productparameters (premium, discount, time duration, trip count limits) and/orthe context constraint itself, the number of times the rider has beenpurchasing the subscription product. The summary statistics of therider's booking behaviors without a subscription may include variablessuch as the number of trips, price of the trips, surge of the trips. Thestatistics may be mean or standard deviation, and the summary level(aggregation level) may be hourly, daily, day of the week, etc. In someembodiments, the average price of historical trips may be an essentialfeature as it plays a vital role in determining if the discount therider gets from a subscription will be larger than the premium the riderpaid to get the subscription. In some embodiments, other features mayalso be considered, such as the number of years the rider has been usingthe service provided by the ride-hailing platform, the most frequentpayment method the rider used, the rider's phone information(manufacturer, operating system, etc.)

After sample matrix X is input into the NN_(feat), a feature matrixX′=NN_(feat)(X) may be obtained, where the feature matrix X′ includes aplurality of feature vectors. In some embodiments, the plurality offeature vectors and the plurality of sets of session-level features maybe fed into a second machine learning model to predict a plurality offirst probabilities for the plurality of historical trip-requestingsessions being converted to trips.

Here, the second machine learning model may refer to the conversionmodel 440. For each trip-requesting session j, p_(conv)(trip|{circumflexover (θ)}_(ŝ(j)), R_(u(j)), B_(j))=[NN_(conv)([X′, B])]_(j), whereNN_(conv) is the neural network model to make use of the feature vectorsin X′ and generate the final conversion probabilities (a binary choice).

In some embodiments, the feature vectors in the feature matrix X′ may berespectively corresponding to a plurality of trip-requesting sessions,where multiple trip-requesting sessions may involve the same rider andthe same subscription product (called a rider-and-subscription-productpair). For example, a rider books several trips within a day to whichthe same subscription product is applicable). In some embodiments, inorder to train the purchase model 450 that predicts the probability ofan individual rider purchasing each of the subscription products, thefeature vectors in the feature matrix X′ may go through a deduplicationprocess to remove the feature vectors with duplicaterider-and-subscription-product pairs to obtain a plurality ofdeduplicated records of features, so that each of the plurality ofdeduplicated records of features corresponds to one individual rider.

In some embodiments, a hash table may be used to perform thededuplication. The hash table may record rider-and-subscription-productpairs in the form of (u, s) tuples. An incoming (u, s) tuple that has aconflict in the hash table may be ignored. This deduplication processmay be denoted as X″=dedup_rows(X′), and the probability of a rider upurchasing a subscription product s may be denoted as:

p _(purchase)(s|{circumflex over (θ)} _(s) , R _(u))=[NN_(purchase)(X″)]_(u), ∀s, u

where NN_(purchase) may refer to the purchase mode 450 (a neuralnetwork).

The feature extractor 430, the conversion model 440, and the purchasemodel 450 may be jointly trained. In some embodiments, three models maybe trained at the same time (e.g., parameters of the three models areadjusted at the same time). In some embodiments, the purchase model 450NN_(purchase) and feature extractor 430 NN_(feat) may be trainedtogether first (460 in FIG. 4), and then the parameters of NN_(feat) maybe fixed to train the conversion model 440 NN_(conv). In someembodiments, the conversion model 440 NN_(conv). and feature extractor430 NN_(feat) may be trained together first (470 in FIG. 4), and thenthe parameters of NN_(feat) may be fixed to train the purchase model 450NN_(purchase).

In some embodiments, the aforementioned prediction models include thefeature extractor 430, the conversion model 440, and the purchase model450 may be implemented with various neural network structures, such asMulti-Layer Perceptrons (MLP) and Restricted Boltzmann Machines (RBM).In some embodiments, these prediction models may be used to constructthe optimization model 310 described in FIG. 3 by constructing one ormore key performance indicator (KPI)s models based on the trained secondmachine learning model and the trained third machine learning model, andconstructing the optimization model with an objective functioncomprising a weighted sum of the KPI models and with the plurality ofparameter vectors as decision variables.

FIG. 5 illustrates an exemplary method 500 for determining acontext-based subscription suite in a ride-hailing platform inaccordance with various embodiments. The method 500 may be performed bya device, apparatus, or system illustrated by FIGS. 1-4. The method 500is merely illustrative. Depending on the implementation, the method 500may have more, fewer, or alternative steps or components. The method 500may be implemented by the computing system 102 in FIG. 1.

Block 510 includes constructing a plurality of parameter vectors eachrepresenting a context-based subscription product, wherein each of theplurality of context-based subscription products corresponds to atransportation context, and when a transportation trip requested througha ride-hailing platform satisfies one of the plurality of transportationcontexts, the corresponding context-based subscription sets a benefit tobe applied to the transportation trip.

Block 520 includes obtaining a plurality of historical trip-requestingsessions between a plurality of riders and the ride-hailing platform,and a plurality of historical context-based subscription productsoffered to the plurality of riders. In some embodiments, each of theplurality of transportation contexts comprises one or more conditions,and the trip satisfies the transportation context when the tripsatisfies the one or more conditions of the transportation context, andthe one or more conditions comprises at least one of the following: aspatial condition defining an origin region or a destination region; aroute condition defining a pair of origin region and destination region;a temporal condition defining a time range; a spatial-temporal conditioncomprising any pair of the spatial condition and the temporal condition;a route-temporal condition comprising any pair of the route conditionand the temporal condition; a weather condition; and a marketplacecondition.

Block 530 includes training, based on the plurality of historicaltrip-requesting sessions and the plurality of context-based historicalsubscription products, at least one key performance indicator (KPI)model that predicts a KPI value for the ride-hailing platform, whereinthe KPI value indicates a reward to the ride-hailing platform foroffering the plurality of context-based subscription products to theplurality of riders of the plurality of historical trip-requestingsessions, and is predicted based at least on the plurality of parametervectors.

In some embodiments, the KPI value to the ride-hailing platform foroffering the plurality of context-based subscription products to theplurality of riders is determined by accumulating a plurality ofsession-level rewards respectively corresponding to the plurality ofcontext-based subscription products; and each of the plurality ofsession-level rewards is determined by: determining, from an output of afirst trained machine learning model, a first probability of a riderinvolved in a historical trip-requesting session subscribing to one ofthe plurality of context-based subscription products; determining, froman output of a second trained machine learning model, a secondprobability of the rider converting the historical trip-requestingsession to a trip; determining an expected reward based on an assumptionthat the historical trip-requesting session is converted to the trip;and determining the session-level reward as a product of the firstprobability, the second probability, and the expected reward.

In some embodiments, the first probability is determined based at leaston the plurality of rider-level features of the rider and the parametervector of the context-based subscription product. In some embodiments,the historical trip-requesting session satisfies one of the plurality oftransportation contexts, and the second probability is determined basedat least on the plurality of session-level features of the historicaltrip-requesting session, the plurality of rider-level features of therider, and the parameter vector of the context-based subscriptionproduct corresponding to the one transportation context.

In some embodiments, the plurality of session-level features comprise atleast one of the following: a price displayed by the ride-hailingplatform to the rider. In some embodiments, a discount displayed by theride-hailing platform to the rider; estimated time to arrive (ETA) forpick-up; estimated route distance to a destination; time; or origin anddestination locations. the rider-level features comprise at least one ofthe following: an average price of historical trips; or the rider'ssubscription history.

Block 540 includes constructing an optimization model to maximize aweighted sum of the at least one KPI value generated by the at least oneKPI model, wherein the optimization model comprises the plurality ofparameter vectors as decision variables.

Block 550 includes determining, by the computing device solving theoptimization model, an optimal configuration for each of the pluralityof parameter vectors, wherein the optimal configuration comprises anoptimal premium and an optimal benefit of the context-based subscriptionproduct corresponding to the each parameter vector.

In some embodiments, the method 500 may further comprise for a givenrider, determining, by the computing device based on the given rider'shistorical trips, one or more of the plurality of transportationcontexts that the given rider's historical trips satisfy; and sending,by the computing device, one or more of the plurality of context-basedsubscription products corresponding to the one or more determinedtransportation contexts to a computing device of the given rider forpurchasing, wherein the one or more of the plurality of context-basedsubscription products are configured based on the corresponding optimalvalues.

FIG. 6 illustrates another exemplary method 600 for determining acontext-based subscription suite in a ride-hailing platform inaccordance with various embodiments. The method 600 may be implementedin an environment shown in FIG. 1. The method 600 may be performed by adevice, apparatus, or system illustrated by FIGS. 1-5, such as thesystem 102 in FIG. 1. Depending on the implementation, the method 600may include additional, fewer, or alternative steps performed in variousorders or in parallel.

Block 610 includes obtaining a plurality of historical trip-requestingsessions between a plurality of riders and a ride-hailing platform, anda plurality of historical context-based subscription products offered tothe plurality of riders, wherein the plurality of historicalcontext-based subscription products respectively correspond to aplurality of transportation contexts, and when a trip requested by atrip-requesting session satisfies one of the plurality of transportationcontexts, the corresponding context-based subscription applies a benefitto the trip.

Block 620 includes feeding the plurality of historical trip-requestingsessions and the plurality of historical context-based subscriptionproducts into a first machine learning model to generate a plurality offeature vectors respectively corresponding to the plurality ofhistorical trip-requesting sessions.

Block 630 includes feeding the plurality of feature vectors to a secondmachine learning model to predict a plurality of first probabilities forthe plurality of historical trip-requesting sessions being converted totrips. In some embodiments, the feeding the plurality of feature vectorsto a second machine learning model comprises: for each of the pluralityof historical trip-requesting sessions, feeding the correspondingfeature vector and a plurality of session-level features of thehistorical trip-requesting session to the second machine learning model,wherein the plurality of session-level features comprises at least oneof the following: a price displayed by the ride-hailing platform to arider; a discount displayed by the ride-hailing platform to the rider;estimated time to arrive (ETA) for pick-up; estimated route distance todestination; time; or origin and destination locations. In someembodiments, each of the plurality of feature vectors comprises a set ofrider-level features of the corresponding historical trip-requestingsession and a parameter vector representing a context-based subscriptionproduct that provides a discount to the corresponding historicaltrip-requesting session. In some embodiments, the plurality ofrider-level features of the rider comprises at least one of thefollowing: an average price of the rider's historical trips; or therider's subscription history.

Block 640 includes deduplicating the plurality of feature vectors bykeeping one of multiple feature vectors corresponding to a same riderand a same subscription product to obtain a plurality of deduplicatedfeature vectors respectively corresponding to the plurality of riders.In some embodiments, the deduplicating the plurality of feature vectorscomprises: generating a hash key based on therider-and-subscription-product pair in each of the plurality of featurevectors; checking whether a hash table comprises the hash key; inresponse to the hash table not comprising the hash key, storing thefeature vector in the hash table indexed by the hash key; and inresponse to the hash table comprising the hash key, skipping the featurevector.

Block 650 includes feeding the plurality of deduplicated feature vectorsto a third machine learning model to predict a plurality of secondprobabilities of the plurality of riders subscribing one or more of theplurality of subscription products.

Block 650 includes jointly training the first machine learning model,the second machine learning model, and the third machine learning modelbased on the plurality of first probabilities, the plurality of secondprobabilities, and the plurality of historical trip-requesting sessions.In some embodiments, the jointly training the first machine learningmodel, the second machine learning model, and the third machine learningmodel comprises: training the first machine learning model and thesecond machine learning model together; and training the third machinelearning model based on the trained first machine learning model. Insome embodiments, the jointly training the first machine learning model,the second machine learning model, and the third machine learning modelcomprises: training the first machine learning model and the thirdmachine learning model together; and training the second machinelearning model based on the trained first machine learning model.

Block 650 includes constructing an optimization model based on thetrained first machine learning model, the trained second machinelearning model, and the trained third machine learning model to searchfor optimal values of a plurality of parameter vectors representing aplurality of optimal subscription products. In some embodiments, theconstructing one or more key performance indicator (KPI)s models basedon the trained second machine learning model and the trained thirdmachine learning model; and constructing the optimization model with anobjective function comprising a weighted sum of the KPI models and withthe plurality of parameter vectors as decision variables. In someembodiments, each of the plurality of parameter vectors defines one ofthe plurality of subscription products and comprises at least one of thefollowing values: a premium of the one subscription product; a discountamount or percentage of the one subscription product; a period ofvalidity of the one subscription product; or a trip count limit of theone subscription product. In some embodiments, the KPI models compriseat least one of the following: a net income, a gross merchandise value(GMV), or a number of trips.

In some embodiments, each of the first machine learning model, thesecond machine learning model, and the third machine learning modelcomprises: a Multi-layer Perceptrons (MLP) neural network; or aRestricted Boltzmann Machines (RBM) neural network.

FIG. 7 illustrates an example computing device in which any of theembodiments described herein may be implemented. The computing devicemay be used to implement one or more components of the systems and themethods shown in FIGS. 1-7. The computing device 700 may comprise a bus702 or other communication mechanism for communicating information andone or more hardware processors 704 coupled with bus 702 for processinginformation. Hardware processor(s) 704 may be, for example, one or moregeneral purpose microprocessors.

The computing device 700 may also include a main memory 707, such as arandom-access memory (RAM), cache and/or other dynamic storage devices710, coupled to bus 702 for storing information and instructions to beexecuted by processor(s) 704. Main memory 707 also may be used forstoring temporary variables or other intermediate information duringexecution of instructions to be executed by processor(s) 704. Suchinstructions, when stored in storage media accessible to processor(s)704, may render computing device 700 into a special-purpose machine thatis customized to perform the operations specified in the instructions.Main memory 707 may include non-volatile media and/or volatile media.Non-volatile media may include, for example, optical or magnetic disks.Volatile media may include dynamic memory. Common forms of media mayinclude, for example, a floppy disk, a flexible disk, hard disk, solidstate drive, magnetic tape, or any other magnetic data storage medium, aCD-ROM, any other optical data storage medium, any physical medium withpatterns of holes, a RAM, a DRAM, a PROM, and EPROM, a FLASH-EPROM,NVRAM, any other memory chip or cartridge, or networked versions of thesame.

The computing device 700 may implement the techniques described hereinusing customized hard-wired logic, one or more ASICs or FPGAs, firmwareand/or program logic which in combination with the computing device maycause or program computing device 700 to be a special-purpose machine.According to one embodiment, the techniques herein are performed bycomputing device 700 in response to processor(s) 704 executing one ormore sequences of one or more instructions contained in main memory 707.Such instructions may be read into main memory 707 from another storagemedium, such as storage device 710. Execution of the sequences ofinstructions contained in main memory 707 may cause processor(s) 704 toperform the process steps described herein. For example, theprocesses/methods disclosed herein may be implemented by computerprogram instructions stored in main memory 707. When these instructionsare executed by processor(s) 704, they may perform the steps as shown incorresponding figures and described above. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The computing device 700 also includes a communication interface 717coupled to bus 702. Communication interface 717 may provide a two-waydata communication coupling to one or more network links that areconnected to one or more networks. As another example, communicationinterface 717 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN (or WAN component tocommunicate with a WAN). Wireless links may also be implemented.

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Each of the processes, methods, and algorithms described in thepreceding sections may be embodied in, and fully or partially automatedby, code modules executed by one or more computer systems or computerprocessors comprising computer hardware. The processes and algorithmsmay be implemented partially or wholly in application-specificcircuitry.

When the functions disclosed herein are implemented in the form ofsoftware functional units and sold or used as independent products, theycan be stored in a processor executable non-volatile computer readablestorage medium. Particular technical solutions disclosed herein (inwhole or in part) or aspects that contribute to current technologies maybe embodied in the form of a software product. The software product maybe stored in a storage medium, comprising a number of instructions tocause a computing device (which may be a personal computer, a server, anetwork device, and the like) to execute all or some steps of themethods of the embodiments of the present application. The storagemedium may comprise a flash drive, a portable hard drive, ROM, RAM, amagnetic disk, an optical disc, another medium operable to store programcode, or any combination thereof.

Particular embodiments further provide a system comprising a processorand a non-transitory computer-readable storage medium storinginstructions executable by the processor to cause the system to performoperations corresponding to steps in any method of the embodimentsdisclosed above. Particular embodiments further provide a non-transitorycomputer-readable storage medium configured with instructions executableby one or more processors to cause the one or more processors to performoperations corresponding to steps in any method of the embodimentsdisclosed above.

Embodiments disclosed herein may be implemented through a cloudplatform, a server or a server group (hereinafter collectively the“service system”) that interacts with a client. The client may be aterminal device, or a client registered by a user at a platform, whereinthe terminal device may be a mobile terminal, a personal computer (PC),and any device that may be installed with a platform applicationprogram.

The various features and processes described above may be usedindependently of one another or may be combined in various ways. Allpossible combinations and sub-combinations are intended to fall withinthe scope of this disclosure. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed, ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The exemplary systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

The various operations of exemplary methods described herein may beperformed, at least partially, by an algorithm. The algorithm may becomprised in program codes or instructions stored in a memory (e.g., anon-transitory computer-readable storage medium described above). Suchalgorithm may comprise a machine learning algorithm. In someembodiments, a machine learning algorithm may not explicitly programcomputers to perform a function but can learn from training data to makea prediction model that performs the function.

The various operations of exemplary methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented enginesthat operate to perform one or more operations or functions describedherein.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented engines. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented engines may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented engines may be distributed across a number ofgeographic locations.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the subject matter has been described withreference to specific example embodiments, various modifications andchanges may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the subject matter may be referred to herein, individually orcollectively, by the term “invention” merely for convenience and withoutintending to voluntarily limit the scope of this application to anysingle disclosure or concept if more than one is, in fact, disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Any process descriptions, elements, or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or steps in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved, as would be understood by those skilled in the art.

As used herein, “or” is inclusive and not exclusive, unless expresslyindicated otherwise or indicated otherwise by context. Therefore,herein, “A, B, or C” means “A, B, A and B, A and C, B and C, or A, B,and C,” unless expressly indicated otherwise or indicated otherwise bycontext. Moreover, “and” is both joint and several, unless expresslyindicated otherwise or indicated otherwise by context. Therefore,herein, “A and B” means “A and B, jointly or severally,” unlessexpressly indicated otherwise or indicated otherwise by context.Moreover, plural instances may be provided for resources, operations, orstructures described herein as a single instance. Additionally,boundaries between various resources, operations, engines, and datastores are somewhat arbitrary, and particular operations are illustratedin a context of specific illustrative configurations. Other allocationsof functionality are envisioned and may fall within a scope of variousembodiments of the present disclosure. In general, structures andfunctionality presented as separate resources in the exampleconfigurations may be implemented as a combined structure or resource.Similarly, structures and functionality presented as a single resourcemay be implemented as separate resources. These and other variations,modifications, additions, and improvements fall within a scope ofembodiments of the present disclosure as represented by the appendedclaims. The specification and drawings are, accordingly, to be regardedin an illustrative rather than a restrictive sense.

The term “include” or “comprise” is used to indicate the existence ofthe subsequently declared features, but it does not exclude the additionof other features. Conditional language, such as, among others, “can,”“could,” “might,” or “may,” unless specifically stated otherwise, orotherwise understood within the context as used, is generally intendedto convey that certain embodiments include, while other embodiments donot include, certain features, elements and/or steps. Thus, suchconditional language is not generally intended to imply that features,elements and/or steps are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without user input or prompting, whether thesefeatures, elements and/or steps are included or are to be performed inany particular embodiment.

1. A computer-implemented method, comprising: constructing, by acomputing device of the ride-hailing platform, a plurality of parametervectors each representing a context-based subscription product, whereineach of the plurality of context-based subscription products correspondsto a transportation context, and when a transportation trip requestedthrough a ride-hailing platform satisfies one of the plurality oftransportation contexts, the corresponding context-based subscriptionsets a benefit to be applied to the transportation trip; obtaining, bythe computing device, a plurality of historical data comprising aplurality of historical trip-requesting sessions between a plurality ofriders and the ride-hailing platform, a plurality of historicalcontext-based subscription products offered to the plurality of riders,and respective labels; jointly training a feature extractor network, afirst neural network, and a second neural network based on the pluralityof historical data, wherein the training comprises: feeding theplurality of historical data into the feature extractor network,obtaining, from the feature extractor network, a plurality of featurevectors representing each of the plurality of historical trip-requestingsessions, feeding the plurality of feature vectors into the first neuralnetwork to predict a plurality of first probabilities for the pluralityof historical trip-requesting sessions being converted to trips, feedingthe plurality of feature vectors into the second neural network topredict a plurality of second probabilities of the plurality of riderssubscribing to one or more of the plurality of subscription products,and adjusting parameters of the feature extractor network, the firstneural network, and the second neural network based on the respectivelabels, the plurality of first probabilities, and the plurality ofsecond probabilities; constructing, by the computing device based on thetrained first neural network and the second neural network, at least onekey performance indicator (KPI) model that predicts a KPI value for theride-hailing platform; constructing, by the computing device, anoptimization model to maximize a weighted sum of the at least one KPIvalue generated by the at least one KPI model, wherein the optimizationmodel comprises the plurality of parameter vectors as decisionvariables; and determining, by the computing device solving theoptimization model, an optimal configuration for each of the pluralityof parameter vectors, wherein the optimal configuration comprises anoptimal premium and an optimal benefit of the context-based subscriptionproduct corresponding to the each parameter vector.
 2. The method ofclaim 1, further comprising: for a given rider, determining, by thecomputing device of the ride-hailing platform based on the given rider'shistorical trips, one or more of the plurality of transportationcontexts that the given rider's historical trips satisfy; and sending,by the computing device of the ride-hailing platform, one or more of theplurality of context-based subscription products corresponding to theone or more determined transportation contexts to a computing device ofthe given rider for purchasing, wherein the one or more of the pluralityof context-based subscription products are configured based on thecorresponding optimal values.
 3. The method of claim 1, wherein thetransportation context comprises one or more conditions, and thetransportation trip satisfies the transportation context when thetransportation trip satisfies the one or more conditions of thetransportation context, and the one or more conditions comprises atleast one of the following: a spatial condition defining an originregion or a destination region; a route condition defining an originregion and a destination region; a temporal condition defining a timerange; a spatial-temporal condition comprising any pair of the spatialcondition and the temporal condition; a route-temporal conditioncomprising any pair of the route condition and the temporal condition; aweather condition; and a marketplace condition.
 4. The method of claim1, wherein: the KPI value indicates a reward to the ride-hailingplatform for offering the plurality of context-based subscriptionproducts to the plurality of riders of the plurality of historicaltrip-requesting sessions, wherein the KPI value is determined byaccumulating a plurality of session-level rewards respectivelycorresponding to the plurality of context-based subscription products;and each of the plurality of session-level rewards is determined by:determining, from an output of the first neural network, a firstprobability of a rider involved in a historical trip-requesting sessionsubscribing to one of the plurality of context-based subscriptionproducts; determining, from an output of a second neural network, asecond probability of the rider converting the historicaltrip-requesting session to a trip; determining an expected reward whenthe historical trip-requesting session is converted to the trip; anddetermining the session-level reward as a product of the firstprobability, the second probability, and the expected reward.
 5. Themethod of claim 4, wherein the first probability is determined based atleast on a plurality of rider-level features of the rider and theparameter vector of the context-based subscription product.
 6. Themethod of claim 4, wherein the historical trip-requesting sessionsatisfies one of the plurality of transportation contexts, and thesecond probability is determined based at least on a plurality ofsession-level features of the historical trip-requesting session, aplurality of rider-level features of the rider, and the parameter vectorof the context-based subscription product corresponding to the onetransportation context.
 7. The method of claim 6, wherein the pluralityof session-level features comprise at least one of the following: aprice displayed by the ride-hailing platform to a computing device ofthe rider; a discount displayed by the ride-hailing platform to thecomputing device of the rider; estimated time to arrive (ETA) forpick-up; an estimated route distance to a destination; time; an originlocation; and a destination location.
 8. The method of claim 6, whereinthe rider-level features comprise at least one of the following: anaverage price of historical trips; and the rider's subscription history.9. The method of claim 1, wherein the at least one KPI model comprisesat least one of the following: a net income model, a gross merchandisevalue (GMV) model, or a number of trips model.
 10. The method of claim1, wherein each of the plurality of parameter vectors representing acontext-based subscription product comprises at least one of thefollowing values: a premium of the context-based subscription product; abenefit amount or percentage of the context-based subscription product;a period of validity of the context-based subscription product; and atrip count limit of the context-based subscription product.
 11. Themethod of claim 1, wherein the plurality of transportation contexts arenon-overlapping so that each of the transportation contexts has only oneapplicable subscription product.
 12. The method of claim 1, wherein oneof the at least one KPI models generates non-summable values, and theweighted sum of the at least one KPI models is determined by: dividingthe non-summable KPI models into a plurality of summable KPI models;determining a plurality of KPI values for the plurality of summable KPImodels based on the plurality of historical trip-requesting sessions;and obtaining a value of the non-summable KPI models based on theplurality of KPI values.
 13. The method of claim 1, wherein theoptimization model is solved by one of the following algorithms: gridsearch; greedy search; and Bayesian optimization.
 14. A systemcomprising one or more processors and one or more non-transitorycomputer-readable memories coupled to the one or more processors, theone or more non-transitory computer-readable memories storinginstructions that, when executed by the one or more processors, causethe system to perform operations comprising: constructing a plurality ofparameter vectors each representing a context-based subscriptionproduct, wherein each of the plurality of context-based subscriptionproducts corresponds to a transportation context, and when atransportation trip requested through a ride-hailing platform satisfiesone of the plurality of transportation contexts, the correspondingcontext-based subscription sets a benefit to be applied to thetransportation trip; obtaining a plurality of historical data comprisinga plurality of historical trip-requesting sessions between a pluralityof riders and the ride-hailing platform, a plurality of historicalcontext-based subscription products offered to the plurality of riders,and respective labels; jointly training a feature extractor network, afirst neural network, and a second neural network based on the pluralityof historical data, wherein the training comprises: feeding theplurality of historical data into the feature extractor network,obtaining, from the feature extractor network, a plurality of featurevectors representing each of the plurality of historical trip-requestingsessions, feeding the plurality of feature vectors into the first neuralnetwork to predict a plurality of first probabilities for the pluralityof historical trip-requesting sessions being converted to trips, feedingthe plurality of feature vectors into the second neural network topredict a plurality of second probabilities of the plurality of riderssubscribing to one or more of the plurality of subscription products,and adjusting parameters of the feature extractor networks, the firstneural network, and the second neural network based on the respectivelabels, the plurality of first probabilities, and the plurality ofsecond probabilities; constructing based on the trained first neuralnetwork and the second neural network, at least one key performanceindicator (KPI) model that predicts a KPI value for the ride-hailingplatform. constructing an optimization model to maximize a weighted sumof the at least one KPI value generated by the at least one KPI model,wherein the optimization model comprises the plurality of parametervectors as decision variables; and determining, by the computing devicesolving the optimization model, an optimal configuration for each of theplurality of parameter vectors, wherein the optimal configurationcomprises an optimal premium and an optimal benefit of the context-basedsubscription product corresponding to the each parameter vector.
 15. Thesystem of claim 14, wherein the operations further comprise: for a givenrider, determining, based on the given rider's historical trips, one ormore of the plurality of transportation contexts that the given rider'shistorical trips satisfy; and sending one or more of the plurality ofcontext-based subscription products corresponding to the one or moredetermined transportation contexts to a computing device of the givenrider for purchasing, wherein the one or more of the plurality ofcontext-based subscription products are configured based on thecorresponding optimal values.
 16. The system of claim 14, wherein eachof the plurality of transportation contexts comprises one or moreconditions, and the trip satisfies the transportation context when thetrip satisfies the one or more conditions of the transportation context,and the one or more conditions comprises at least one of the following:a spatial condition defining an origin region or a destination region; aroute condition defining an origin region and a destination region; atemporal condition defining a time range; a spatial-temporal conditioncomprising any pair of the spatial condition and the temporal condition;a route-temporal condition comprising any pair of the route conditionand the temporal condition; a weather condition; and a marketplacecondition.
 17. A non-transitory computer-readable storage medium storinginstructions that, when executed by one or more processors, cause theone or more processors to perform operations comprising: constructing aplurality of parameter vectors each representing a context-basedsubscription product, wherein each of the plurality of context-basedsubscription products corresponds to a transportation context, and whena transportation trip requested through a ride-hailing platformsatisfies one of the plurality of transportation contexts, thecorresponding context-based subscription sets a benefit to be applied tothe transportation trip; obtaining a plurality of historical datacomprising a plurality of historical trip-requesting sessions between aplurality of riders and the ride-hailing platform, [[and]], a pluralityof historical context-based subscription products offered to theplurality of riders, and respective labels; jointly training a featureextractor network, a first neural network, and a second neural networkbased on the plurality of historical data, wherein the trainingcomprises: feeding the plurality of historical data into the featureextractor network, obtaining, from the feature extractor network, aplurality of feature vectors representing each of the plurality ofhistorical trip-requesting sessions, feeding the plurality of featurevectors into the first neural network to predict a plurality of firstprobabilities for the plurality of historical trip-requesting sessionsbeing converted to trips, feeding the plurality of feature vectors intothe second neural network to predict a plurality of second probabilitiesof the plurality of riders subscribing to one or more of the pluralityof subscription products, and adjusting parameters of the featureextractor network, the first neural network, and the second neuralnetwork based on the respective labels, the plurality of firstprobabilities, and the plurality of second probabilities; constructingbased on the trained first neural network and the second neural network,at least one key performance indicator (KPI) model that predicts a KPIvalue for the ride-hailing platform; constructing an optimization modelto maximize a weighted sum of the at least one KPI value generated bythe at least one KPI model, wherein the optimization model comprises theplurality of parameter vectors as decision variables; and determining,by the computing device solving the optimization model, an optimalconfiguration for each of the plurality of parameter vectors, whereinthe optimal configuration comprises an optimal premium and an optimalbenefit of the context-based subscription product corresponding to theeach parameter vector.
 18. The storage medium of claim 17, wherein theoperations further comprise: for a given rider, determining, based onthe given rider's historical trips, one or more of the plurality oftransportation contexts that the given rider's historical trips satisfy;and sending one or more of the plurality of context-based subscriptionproducts corresponding to the one or more determined transportationcontexts to a computing device of the given rider for purchasing,wherein the one or more of the plurality of context-based subscriptionproducts are configured based on the corresponding optimal values. 19.The storage medium of claim 17, wherein the at least one KPI modelscomprise at least one of the following: a net income model, a grossmerchandise value (GMV) model, or a number of trips model.
 20. Thestorage medium of claim 17, wherein each of the plurality oftransportation contexts comprises one or more conditions, and the tripsatisfies the transportation context when the trip satisfies the one ormore conditions of the transportation context, and the one or moreconditions comprises at least one of the following: a spatial conditiondefining an origin region or a destination region; a route conditiondefining an origin region and a destination region; a temporal conditiondefining a time range; a spatial-temporal condition comprising any pairof the spatial condition and the temporal condition; a route-temporalcondition comprising any pair of the route condition and the temporalcondition; a weather condition; and a marketplace condition.